A Stop-skipping Pattern for Metro Trains using Passenger-oriented optimization strategy

Energy-efficient timetable optimization has attracted much attention recently to reduce the energy consumption and the associated costs of metro systems. Compared with all-stop patterns, stop-skipping patterns potentially lead to decreasing energy consumption. This paper develops a passenger-based approach to design energy-efficient metro timetables and speed profiles with a stop-skipping pattern based on smart-card data. First, we develop an algorithm to generate: a set of likely to be skipped stations is identified according to historical passenger demands, and a heuristic rule is adopted to select the specific skipped stations for each train. Second, we reformulate the initial optimization problem as a convex quadratic programming problem and develop a solution algorithm to determine the optimized timetables and speed profiles. A numerical example is conducted by the train operational data of a metro line in Beijing (China). The results show that the developed approach reduces energy consumption by 11.38% in comparison with the current timetable.